Giskard enables confidential and Byzantine-robust decentralized machine learning aggregation by organizing parties into tree-based committees of size O(log n). It uses BGW-style MPC and a committee-adapted binary search to compute an approximate median, reducing per-party communication complexity asymptotically while maintaining model utility under up to n/4 Byzantine parties.
OrthoReg introduces orthogonal regularization to prevent neural components from relearning symbolic structures in hybrid dynamical systems. By directly penalizing overlap between symbolic and neural parts, it enables a complementary decomposition where symbolic models capture expressible physics and neural models handle remaining dynamics. On benchmarks with partial library mismatch, OrthoReg improves symbolic recovery and out-of-distribution performance.
The paper introduces local certificates that provide two-sided confidence bands for population-risk increments around a current model. The upper endpoint of this band defines a risk-controlled update rule: updates are accepted only if the certified upper endpoint is nonpositive, otherwise the current model is retained.
Morpheus is a morphology-aware neural tokenizer and word embedder for Turkish that preserves original text through lossless encoding and decoding. It achieves the lowest bits-per-character (1.425), improves morphological alignment (MorphScore macro-F1 0.61), and uses 19% less GPU memory than 64K-vocabulary subword tokenizers. Frozen Morpheus embeddings outperform BGE-M3 and BERTurk in lexical retrieval, with root-family MAP of 0.85 and ROC-AUC of 1.00.
LegalWorld models Chinese civil litigation as a causally connected chain of five stages, based on 75,309 judgments. It includes reusable infrastructure to maintain consistency across stages and enables LongJud-Bench to evaluate agent performance across all phases, revealing significant capability gaps between models in different legal tasks.
Graph-ESBMC-PLC enables formal verification of graphical IEC 61131-3 Ladder Diagram programs by introducing a DFS-based resolver that converts graphical LD connections into valid GOTO intermediate representation. Validation on three real-world programs shows full IR generation and successful verification of safety properties at k=2 within 70ms, with no regression on textual benchmarks.
This study finds that methodological information in research papers is unevenly distributed, with middle-to-late and final segments showing greater discriminative power. Combining these segments with bibliographic metadata improves the accuracy of automatic research method classification in library and information science.
A meta-feedback semi-supervised learning framework enables scaling of automatic emergency braking using massive unlabeled fleet data. The stabilized approach reduces pseudo-label errors through noise-aware decoupling and kinematics-gated pseudo-labeling, improving safety with a 100:1 positive-to-false activation ratio and 35% more accident-free driving mileage compared to rule-based systems.
A domain-shift aware neural network is proposed for estimating unbalance masses in rotating systems under varying conditions. The model uses maximum mean discrepancy to align feature representations across different operating domains, improving prediction accuracy when system behaviors differ from training conditions. Results show its effectiveness in structural health monitoring applications.
TransitNet, a compact attention-augmented deep learning framework, achieves 95.2% accuracy in low-SNR transit blind searches, outperforming TLS and BLS in ROC-AUC and PR-AP values. It recovers 93.0% of injected Earth- and sub-Earth-size transits, with 97.4% of injected transits fully covered by estimated transit windows, and successfully recovers all 34 confirmed Kepler planets with a mean midpoint error of 1.24 hours.
An exploratory analysis of 10 vibe-coded C/C++ projects reveals near-zero in-artifact variability, with all decisions resolved at generation time. The paper proposes Variability by Regeneration (VbR), a product-line approach where an LLM acts as a derivation engine, generating tailored binaries from declarative specifications, with a variant dispatcher routing user requests to the correct binary. VbR shifts variability into specifications, not code, offering a new paradigm for SPL engineering.
A study uses SHAP and SSHAP techniques to analyze electricity price drivers in 39 European bidding zones. It finds solar energy has a disproportionate impact on prices, gas remains a dominant factor, and interconnections highlight regional interdependence. The research also builds a synthetic EU-wide market to examine a fully integrated scenario.
A new taxonomy classifies LLM agent communication protocols across five dimensions: counterparty, payload, interaction state, discovery mechanism, and schema flexibility. Analysis shows hybrid payloads, session-state persistence, and runtime schema negotiation are common, with decentralized discovery remaining rare. The study predicts short-term convergence toward unified agent-to-agent and agent-to-context protocols, and long-term evolution toward a federated, layered protocol stack.
OrthoReg introduces orthogonal regularization to prevent neural components from relearning symbolic structures in hybrid dynamical systems. By directly penalizing overlap between symbolic and neural parts, it enables a complementary decomposition where symbolic models capture expressible physics and neural components handle remaining dynamics. On benchmarks with partial library mismatch, OrthoReg improves symbolic recovery and out-of-distribution performance.
The article confirms there was no actual jailbreak of Anthropic's Fable AI. Instead, a test involving fake code with planted vulnerabilities was conducted, where Fable refused to review the code and only responded to a request to 'fix this code' after manual steps. Katie Moussouris of Luta Security states this scenario should not trigger export controls, calling it a deliberate, engineered test that undermines claims of a security breach.
The author outlines three core goals: clarifying frontier AI model evolution, building an open AI ecosystem, and creating institutions to support these missions. Interconnects serves as a raw, independent voice for frontier AI thinking, with a dedicated technical audience of over 70K subscribers. The blog maintains paywalled comments to prevent AI-generated noise, and the author plans to reach 1000 paid subscribers by summer, emphasizing financial sustainability and independence amid rising AI service costs.
INI-VPINN is a variational physics-informed neural network that implicitly enforces Neumann and interface conditions using compact support weighting functions and integration by parts. It achieves higher accuracy and faster convergence than existing PINN methods in solving multi-material problems with geometric singularities and mixed boundary conditions, and is publicly available on GitHub.
ConTex reformulates counterfactual generation for time series forecasting as a globally consistent intervention problem. It achieves state-of-the-art validity with sparse, interpretable interventions, reduces computational cost by 12-36x, and enables real-time inference in approximately 0.007 seconds.
This paper proposes SD-ZFS, a deep reinforcement learning framework adapted from S2V-DQN, to solve the NP-hard minimum zero-forcing set problem on undirected graphs. The framework demonstrates strong performance compared to optimal solutions and greedy heuristics, showing effective generalization, scalability, and transfer across diverse graph structures.
A new convex quasilinearization method, LiL-Q, solves nonlinear PDEs by reducing them to linear subproblems using physics-informed neural networks. LiL-Q converges in single-digit iterations across seven benchmarks, achieving machine precision when the exact solution lies in the trial space, and requires up to two orders of magnitude fewer parameters than standard PINN solvers.